718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
132 lines
3.6 KiB
Python
132 lines
3.6 KiB
Python
#!/usr/bin/python
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'''
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This example illustrates how to use cv.HoughCircles() function.
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import cv2 as cv
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import numpy as np
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import sys
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from numpy import pi, sin, cos
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from tests_common import NewOpenCVTests
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def circleApproximation(circle):
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nPoints = 30
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dPhi = 2*pi / nPoints
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contour = []
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for i in range(nPoints):
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contour.append(([circle[0] + circle[2]*cos(i*dPhi),
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circle[1] + circle[2]*sin(i*dPhi)]))
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return np.array(contour).astype(int)
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def convContoursIntersectiponRate(c1, c2):
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s1 = cv.contourArea(c1)
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s2 = cv.contourArea(c2)
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s, _ = cv.intersectConvexConvex(c1, c2)
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return 2*s/(s1+s2)
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class houghcircles_test(NewOpenCVTests):
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def test_houghcircles(self):
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fn = "samples/data/board.jpg"
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src = self.get_sample(fn, 1)
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img = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
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img = cv.medianBlur(img, 5)
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circles = cv.HoughCircles(img, cv.HOUGH_GRADIENT, 1, 10, np.array([]), 100, 30, 1, 30)[0]
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testCircles = [[38, 181, 17.6],
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[99.7, 166, 13.12],
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[142.7, 160, 13.52],
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[223.6, 110, 8.62],
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[79.1, 206.7, 8.62],
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[47.5, 351.6, 11.64],
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[189.5, 354.4, 11.64],
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[189.8, 298.9, 10.64],
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[189.5, 252.4, 14.62],
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[252.5, 393.4, 15.62],
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[602.9, 467.5, 11.42],
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[222, 210.4, 9.12],
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[263.1, 216.7, 9.12],
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[359.8, 222.6, 9.12],
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[518.9, 120.9, 9.12],
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[413.8, 113.4, 9.12],
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[489, 127.2, 9.12],
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[448.4, 121.3, 9.12],
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[384.6, 128.9, 8.62]]
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matches_counter = 0
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for i in range(len(testCircles)):
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for j in range(len(circles)):
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tstCircle = circleApproximation(testCircles[i])
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circle = circleApproximation(circles[j])
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if convContoursIntersectiponRate(tstCircle, circle) > 0.6:
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matches_counter += 1
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self.assertGreater(float(matches_counter) / len(testCircles), .5)
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self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
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def test_houghcircles_alt(self):
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fn = "samples/data/board.jpg"
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src = self.get_sample(fn, 1)
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img = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
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img = cv.medianBlur(img, 5)
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circles = cv.HoughCircles(img, cv.HOUGH_GRADIENT_ALT, 1, 10, np.array([]), 300, 0.9, 1, 30)
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self.assertEqual(circles.shape, (1, 18, 3))
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circles = circles[0]
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testCircles = [[38, 181, 17.6],
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[99.7, 166, 13.12],
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[142.7, 160, 13.52],
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[223.6, 110, 8.62],
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[79.1, 206.7, 8.62],
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[47.5, 351.6, 11.64],
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[189.5, 354.4, 11.64],
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[189.8, 298.9, 10.64],
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[189.5, 252.4, 14.62],
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[252.5, 393.4, 15.62],
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[602.9, 467.5, 11.42],
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[222, 210.4, 9.12],
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[263.1, 216.7, 9.12],
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[359.8, 222.6, 9.12],
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[518.9, 120.9, 9.12],
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[413.8, 113.4, 9.12],
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[489, 127.2, 9.12],
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[448.4, 121.3, 9.12],
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[384.6, 128.9, 8.62]]
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matches_counter = 0
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for i in range(len(testCircles)):
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for j in range(len(circles)):
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tstCircle = circleApproximation(testCircles[i])
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circle = circleApproximation(circles[j])
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if convContoursIntersectiponRate(tstCircle, circle) > 0.6:
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matches_counter += 1
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self.assertGreater(float(matches_counter) / len(testCircles), .5)
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self.assertLess(float(len(circles) - matches_counter) / len(circles), .75)
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if __name__ == '__main__':
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NewOpenCVTests.bootstrap()
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